This paper studies the application of proper orthogonal decomposition (POD) to reduce the order of distributed reactor models\r\nwith axial and radial diffusion and the implementation of model predictive control (MPC) based on discrete-time linear time\r\ninvariant (LTI) reduced-ordermodels. In this paper, the control objective is to keep the operation of the reactor at a desired operating\r\ncondition in spite of the disturbances in the feed flow. This operating condition is determined by means of an optimization algorithm\r\nthat provides the optimal temperature and concentration profiles for the system. Around these optimal profiles, the nonlinear partial\r\ndifferential equations (PDEs), that model the reactor are linearized, and afterwards the linear PDEs are discretized in space giving\r\nas a result a high-order linear model. POD and Galerkin projection are used to derive the low-order linear model that captures the\r\ndominant dynamics of the PDEs, which are subsequently used for controller design. An MPC formulation is constructed on the\r\nbasis of the low-order linear model.The proposed approach is tested through simulation, and it is shown that the results are good\r\nwith regard to keep the operation of the reactor.
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